Distributed radar antenna array aperture

ABSTRACT

A vehicle radar system utilizes multiple radar sensors having overlapping fields of view to effectively synthesize a distributed radar antenna array aperture from the outputs of the multiple radar sensors and effectively enhance one or more of angular resolution, detection range and signal to noise ratio beyond that supported by any of the radar sensors individually.

BACKGROUND

As computing and vehicular technologies continue to evolve,autonomy-related features have become more powerful and widelyavailable, and capable of controlling vehicles in a wider variety ofcircumstances. For automobiles, for example, the automotive industry hasgenerally adopted SAE International standard J3016, which designates 6levels of autonomy. A vehicle with no autonomy is designated as Level 0,and with Level 1 autonomy, a vehicle controls steering or speed (but notboth), leaving the operator to perform most vehicle functions. WithLevel 2 autonomy, a vehicle is capable of controlling steering, speedand braking in limited circumstances (e.g., while traveling along ahighway), but the operator is still required to remain alert and beready to take over operation at any instant, as well as to handle anymaneuvers such as changing lanes or turning. Starting with Level 3autonomy, a vehicle can manage most operating variables, includingmonitoring the surrounding environment, but an operator is stillrequired to remain alert and take over whenever a scenario the vehicleis unable to handle is encountered. Level 4 autonomy provides an abilityto operate without operator input, but only in specific conditions suchas only certain types of roads (e.g., highways) or only certaingeographical areas (e.g., specific cities for which adequate mappingdata exists). Finally, Level 5 autonomy represents a level of autonomywhere a vehicle is capable of operating free of operator control underany circumstances where a human operator could also operate.

The fundamental challenges of any autonomy-related technology relate tocollecting and interpreting information about a vehicle's surroundingenvironment, along with making and implementing decisions toappropriately control the vehicle given the current environment withinwhich the vehicle is operating. Therefore, continuing efforts are beingmade to improve each of these aspects, and by doing so, autonomousvehicles increasingly are able to reliably handle a wider variety ofsituations and accommodate both expected and unexpected conditionswithin an environment.

One particular technology that is increasingly relied upon forcollecting information about a vehicle's surrounding environment isradar, which is based on the emission, reflection and sensing of radiowave electromagnetic radiation within an environment to detect, and insome instances, determine the position and velocity of various objectswithin the environment. Despite continuing improvements to radarperformance, however, both cost and technical limitations continue toexist, so a continuing need exists for improvements to radar technology,and particularly for radar technology used in connection with thecontrol of an autonomous vehicle.

SUMMARY

The present disclosure is generally related to a radar system forautomotive purposes including autonomous vehicles. In particular,multiple radar sensors with overlapping fields of view may be used toeffectively synthesize a distributed radar antenna array aperture fromthe transmitters and receivers of the multiple radar sensors andeffectively enhance one or more of angular resolution, detection rangeand signal to noise ratio beyond that supported by any of the radarsensors individually. In some instances, the techniques described hereinmay be used to enable radar sensors that otherwise would haveinsufficient angular resolution on their own to adequately discriminatebetween various objects in the environment of an autonomous or othervehicle to be used collectively by a vehicle control system inconnection with the autonomous control of a vehicle.

Therefore, consistent with one aspect of the invention, a method mayinclude receiving first radar data from a first multiple input multipleoutput (MIMO) radar sensor disposed on a vehicle, the first MIMO radarsensor including one or more transmit antennas and one or more receiveantennas forming a first radar sub-array, and the first radar dataincluding first point data identifying one or more points sensed by thefirst MIMO radar sensor and first beamforming data from the first radarsub-array, receiving second radar data from a second MIMO radar sensordisposed on the vehicle, the second MIMO radar sensor having a field ofview that overlaps with that of the first MIMO radar sensor andincluding one or more transmit antennas and one or more receive antennasforming a second radar sub-array, and the second radar data includingsecond point data identifying one or more points sensed by the secondMIMO radar sensor and second beamforming data from the second radarsub-array, and synthesizing a distributed array from the first andsecond radar sub-arrays by applying a phase correction that compensatesfor temporal or spatial mismatches between the first and second radarsub-arrays using the first and second point data and the first andsecond beamforming data and thereafter performing a beamformingoperation on one or more points in the first or second point data afterthe phase correction is applied.

In some implementations, the first and second MIMO radar sensors operateusing separate local oscillators. Also, in some implementations, thefirst and second MIMO radar sensors operate using separate clocks.Further, in some implementations, the first point data includes a pointcloud, the point cloud identifying a position and a velocity for each ofthe one or more points sensed by the first MIMO radar sensor.

In some implementations, the first beamforming data includes abeamvector for each of the one or more points sensed by the first MIMOradar sensor and the second beamforming data includes a beamvector foreach of the one or more points sensed by the second MIMO radar sensor.In addition, in some implementations, synthesizing the distributed arrayfurther includes identifying one or more correlated points from thefirst and second point data, and applying the phase correction includesgenerating a set of ideal beamvectors for one of the first and secondradar sensors based upon the identified one or more correlated points,and generating the phase correction by comparing the set of idealbeamvectors to the beamvectors in the first and second beamforming data.

In some implementations, performing the beamforming operation refines aposition of at least one of the one or more points in the first orsecond point data. In addition, in some implementations, performing thebeamforming operation determines an additional point.

Moreover, in some implementations, identifying the one or morecorrelated points is performed using a nearest neighbor spatial matchingalgorithm based on range, Doppler and angle of arrival correspondencebetween points in the first and second point data.

Consistent with another aspect of the invention, a vehicle radar systemmay include a memory, one or more processors, and program code residentin the memory and configured upon execution by the one or moreprocessors to receive first radar data from a first multiple inputmultiple output (MIMO) radar sensor disposed on the vehicle, the firstMIMO radar sensor including one or more transmit antennas and one ormore receive antennas forming a first radar sub-array, and the firstradar data including first point data identifying one or more pointssensed by the first MIMO radar sensor and first beamforming data fromthe first radar sub-array, receive second radar data from a second MIMOradar sensor disposed on the vehicle, the second MIMO radar sensorhaving a field of view that overlaps with that of the first MIMO radarsensor and including one or more transmit antennas and one or morereceive antennas forming a second radar sub-array, and the second radardata including second point data identifying one or more points sensedby the second MIMO radar sensor and second beamforming data from thesecond radar sub-array, and synthesize a distributed array from thefirst and second radar sub-arrays by applying a phase correction thatcompensates for temporal or spatial mismatches between the first andsecond radar sub-arrays using the first and second point data and thefirst and second beamforming data and thereafter performing abeamforming operation on one or more points in the first or second pointdata after the phase correction is applied.

Some implementations may also include the first MIMO radar sensor. Insome implementations, the one or more processors are disposed in thefirst MIMO radar sensor. Some implementations may further include thesecond MIMO radar sensor, where the one or more processors are disposedexternal of each of the first and second MIMO radar sensors. In someimplementations, the first and second MIMO radar sensors operate usingseparate local oscillators. In addition, in some implementations, thefirst and second MIMO radar sensors operate using separate clocks.

In some implementations, the first point data includes a point cloud,the point cloud identifying a position and a velocity for each of theone or more points sensed by the first MIMO radar sensor. Moreover, insome implementations, the first beamforming data includes a beamvectorfor each of the one or more points sensed by the first MIMO radar sensorand the second beamforming data includes a beamvector for each of theone or more points sensed by the second MIMO radar sensor. Also, in someimplementations, the program code is configured to synthesize thedistributed array further by identifying one or more correlated pointsfrom the first and second point data, and the program code is configuredto apply the phase correction by generating a set of ideal beamvectorsfor one of the first and second radar sensors based upon the identifiedone or more correlated points, and generating the phase correction bycomparing the set of ideal beamvectors to the beamvectors in the firstand second beamforming data.

In some implementations, the beamforming operation refines a position ofat least one of the one or more points in the first or second pointdata. In addition, in some implementations, the beamforming operationdetermines an additional point. Also, in some implementations, theprogram code is configured to identify the one or more correlated pointsusing a nearest neighbor spatial matching algorithm based on range,Doppler and angle of arrival correspondence between points in the firstand second point data.

Consistent with another aspect of the invention, a program product mayinclude a non-transitory computer readable medium, and program codestored on the non-transitory computer readable medium and configuredupon execution by one or more processors to receive first radar datafrom a first multiple input multiple output (MIMO) radar sensor disposedon the vehicle, the first MIMO radar sensor including one or moretransmit antennas and one or more receive antennas forming a first radarsub-array, and the first radar data including first point dataidentifying one or more points sensed by the first MIMO radar sensor andfirst beamforming data from the first radar sub-array, receive secondradar data from a second MIMO radar sensor disposed on the vehicle, thesecond MIMO radar sensor having a field of view that overlaps with thatof the first MIMO radar sensor and including one or more transmitantennas and one or more receive antennas forming a second radarsub-array, and the second radar data including second point dataidentifying one or more points sensed by the second MIMO radar sensorand second beamforming data from the second radar sub-array, andsynthesize a distributed array from the first and second radarsub-arrays by applying a phase correction that compensates for temporalor spatial mismatches between the first and second radar sub-arraysusing the first and second point data and the first and secondbeamforming data and thereafter performing a beamforming operation onone or more points in the first or second point data after the phasecorrection is applied.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts described in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example environment in which implementationsdisclosed herein can be implemented.

FIG. 2 illustrates an example implementation of a Multiple InputMultiple Output (MIMO) radar sensor that may be utilized byimplementations disclosed herein.

FIG. 3 illustrates an example virtual antenna array capable of beingproduced by a MIMO radar sensor that may be utilized by implementationsdisclosed herein.

FIG. 4 illustrates an example transmitter channel for the MIMO radarsensor of FIG. 2 .

FIG. 5 illustrates an example receiver channel for the MIMO radar sensorof FIG. 2 .

FIG. 6 illustrates an example process for sensing targets in anenvironment with various implementations disclosed herein.

FIG. 7 illustrates an example implementation of a multi-sensor radarsystem utilizing a distributed radar antenna array aperture consistentwith some aspects of the invention.

FIG. 8 illustrates an example implementation of a method for sensingobjects in an environment using the multi-sensor radar system of FIG. 7.

DETAILED DESCRIPTION

The herein-described implementations are generally directed to variousimprovements associated with multiple input multiple output (MIMO) radarsensors, e.g., for use in connection with the control of an autonomousor other type of vehicle, among other applications. Prior to discussingsuch improvements, however, a brief discussion of an autonomous vehicleenvironment and of MIMO radar sensors are provided below.

Autonomous Vehicle Environment

Turning to the Drawings, wherein like numbers denote like partsthroughout the several views, FIG. 1 illustrates an autonomous vehicle100 suitable for utilizing the various techniques described herein.Vehicle 100, for example, may include a powertrain 102 including a primemover 104 powered by an energy source 106 and capable of providing powerto a drivetrain 108, as well as a control system 110 including adirection control 112, a powertrain control 114, and brake control 116.Vehicle 100 may be implemented as any number of different types ofvehicles, including vehicles capable of transporting one or both ofpeople and cargo, and it will be appreciated that the aforementionedcomponents 102-116 may vary widely based upon the type of vehicle withinwhich these components are utilized.

The implementations discussed hereinafter, for example, will focus on awheeled land vehicle such as a car, van, truck, bus, etc. In suchimplementations, the prime mover 104 may include one or more electricmotors, an internal combustion engine, or a combination thereof (amongothers). The energy source 106 may include, for example, one or more ofa fuel system (e.g., providing gasoline, diesel, hydrogen, etc.), abattery system, solar panels or other renewable energy source, and afuel cell system. Drivetrain 108 may include one or more of wheels,tires, a transmission and any other mechanical drive components suitablefor converting the output of prime mover 104 into vehicular motion, aswell as one or more brakes configured to controllably stop or slow thevehicle 100 and direction or steering components suitable forcontrolling the trajectory of the vehicle 100 (e.g., a rack and pinionsteering linkage enabling one or more wheels of vehicle 100 to pivotabout a generally vertical axis to vary an angle of the rotationalplanes of the wheels relative to the longitudinal axis of the vehicle).In some implementations, combinations of powertrains and energy sourcesmay be used (e.g., in the case of electric/gas hybrid vehicles), and insome instances multiple electric motors (e.g., dedicated to individualwheels or axles) may be used as a prime mover.

Direction control 112 may include one or more actuators, one or moresensors, or a combination thereof for controlling and receiving feedbackfrom the direction or steering components to enable the vehicle 100 tofollow a desired trajectory. Powertrain control 114 may be configured tocontrol the output of powertrain 102, e.g., to control the output powerof prime mover 104, to control a gear of a transmission in drivetrain108, etc., thereby controlling one or more of a speed and direction ofthe vehicle 100. Brake control 116 may be configured to control one ormore brakes that slow or stop vehicle 100, e.g., disk or drum brakescoupled to the wheels of the vehicle.

Other vehicle types will necessarily utilize different powertrains,drivetrains, energy sources, direction controls, powertrain controls andbrake controls, as will be appreciated by those of ordinary skill havingthe benefit of the instant disclosure. Moreover, in some implementationssome of the components may be combined, e.g., where directional controlof a vehicle is primarily handled by varying an output of one or moreprime movers. Therefore, implementations disclosed herein not limited tothe particular application of the herein-described techniques in anautonomous wheeled land vehicle.

In the illustrated implementation, full or semi-autonomous control overvehicle 100 is implemented in a vehicle control system 120, which mayinclude one or more processors 122 and one or more memories 124, witheach processor 122 configured to execute program code instructions 126stored in a memory 124. The processor(s) 122 may include, for example,one or more graphics processing units (GPUs), one or more centralprocessing units (CPUs), or a combination thereof.

Sensors 130 may include various sensors suitable for collectinginformation from a vehicle's surrounding environment for use incontrolling the operation of the vehicle. For example, sensors 130 mayinclude one or more Radio Detection and Ranging (RADAR) sensors, withwhich a number of the techniques described herein may be implemented.

Sensors 130 may also optionally include one or more Light Detection andRanging (LIDAR) sensors 132, as well as one or more satellite navigation(SATNAV) sensors 138, e.g., compatible with any of various satellitenavigation systems such as GPS, GLONASS, Galileo, Compass, etc. EachSATNAV sensor 138 may be used to determine the location of the vehicleon the Earth using satellite signals. Sensors 130 may also optionallyinclude one or more cameras 140, one or more inertial measurement units(IMUS) 142, one or more wheel encoders 144, or a combination thereof.Each camera 140 may be a monographic or stereographic camera and mayrecord one or more of still and video imagers. Each IMU 142 may includemultiple gyroscopes and accelerometers capable of detecting linear androtational motion of the vehicle 100 in three directions. Wheel encoders144 may be used to monitor the rotation of one or more wheels of vehicle100.

The outputs of sensors 130 may be provided to a set of controlsubsystems 150, including, for example, a localization subsystem 152, aperception subsystem 154, a planning subsystem 156, and a controlsubsystem 158. As will become more apparent hereinafter, radar sensors132 may be used by one or more of such subsystems 152-158 to control anautonomous vehicle.

Localization subsystem 152 may be principally responsible for preciselydetermining the location and orientation (also sometimes referred to as“pose”) of vehicle 100 within its surrounding environment, and generallywithin some frame of reference.

Perception subsystem 154 may be principally responsible for detecting,tracking and identifying elements within the environment surroundingvehicle 100. For example, perception subsystem 154 may, at each of aplurality of iterations, determine a pose, classification, and velocityfor each of one or more objects in the environment surrounding vehicle100. Further, for example, the perception subsystem 154 may trackvarious objects over multiple iterations. For instance, the perceptionsubsystem 154 may track an additional vehicle over multiple iterationsas the additional vehicle moves relative to vehicle 100.

Planning subsystem 156 may be principally responsible for planning atrajectory for vehicle 100 over some timeframe given a desireddestination as well as the static and moving objects within theenvironment. For example, and as described herein, planning subsystem156 may plan a trajectory for vehicle 100 based at least in part on oneor more of a pose, classification, and velocity for each of one or moreobjects in an environment of the vehicle 100 in the environmentsurrounding vehicle 100. In some implementations, planning subsystem 156may plan the trajectory for the vehicle 100 by generating, andconsidering, candidate trajectories for each of one or more additionalmobile objects in the environment. Planning subsystem 156 may determinea candidate trajectory for an object at an iteration based on a pose,classification, velocity, or a combination thereof for the iteration,and may track the object over multiple iterations.

Control subsystem 158 may be principally responsible for generatingsuitable control signals for controlling the various controls in controlsystem 110 in order to implement the planned trajectory of the vehicle100.

It will be appreciated that the collection of components illustrated inFIG. 1 for vehicle control system 120 is merely exemplary in nature.Individual sensors may be omitted in some implementations. Additionallyor alternatively, in some implementations multiple sensors of the typesillustrated in FIG. 1 may be used for redundancy or to cover differentregions around a vehicle, and other types of sensors may be used.Likewise, different types and combinations of control subsystems may beused in other implementations. Further, while subsystems 152-158 areillustrated as being separate from processors 122 and memory 124, itwill be appreciated that in some implementations, some or all of thefunctionality of a subsystem 152-158 may be implemented with programcode instructions 126 resident in one or more memories 124 and executedby one or more processors 122, and that these subsystems 152-158 may insome instances be implemented using the same processors and memory.Subsystems in some implementations may be implemented at least in partusing various dedicated circuit logic, various processors, variousfield-programmable gate arrays (“FPGA”), various application-specificintegrated circuits (“ASIC”), various real time controllers, and thelike, and as noted above, multiple subsystems may utilize circuitry,processors, sensors or other components. Further, the various componentsin vehicle control system 120 may be networked in various manners.

In some implementations, vehicle 100 may also include a secondaryvehicle control system (not illustrated), which may be used as aredundant or backup control system for vehicle 100. In someimplementations, the secondary vehicle control system may be capable offully operating autonomous vehicle 100 in the event of an adverse eventin vehicle control system 120, while in other implementations, thesecondary vehicle control system may only have limited functionality,e.g., to perform a controlled stop of vehicle 100 in response to anadverse event detected in primary vehicle control system 120. In stillother implementations, the secondary vehicle control system may beomitted.

In addition, while powertrain 102, control system 110, and vehiclecontrol system 120 are illustrated in FIG. 1 as being separate systems,in other implementations, some of all of these systems may be combinedinto a single system, e.g., with control system 110 and vehicle controlsystem 120 combined into a single autonomous vehicle control system, orusing other combinations. Further, in other implementations, some or allof the functionality illustrated as being within one system in FIG. 1may be implemented in another system.

In general, an innumerable number of different architectures, includingvarious combinations of software, hardware, circuit logic, sensors,networks, etc. may be used to implement the various componentsillustrated in FIG. 1 . Each processor may be implemented, for example,as a microprocessor and each memory may represent the random accessmemory (RAM) devices comprising a main storage, as well as anysupplemental levels of memory, e.g., cache memories, non-volatile orbackup memories (e.g., programmable or flash memories), read-onlymemories, etc. In addition, each memory may be considered to includememory storage physically located elsewhere in vehicle 100, e.g., anycache memory in a processor, as well as any storage capacity used as avirtual memory, e.g., as stored on a mass storage device or on anothercomputer or controller. One or more processors illustrated in FIG. 1 ,or entirely separate processors, may be used to implement additionalfunctionality in vehicle 100 outside of the purposes of autonomouscontrol, e.g., to control entertainment systems, to operate doors,lights, convenience features, etc. Processors may also be implemented inwhole or in part within individual sensors in some implementations.

In addition, for additional storage, vehicle 100 may also include one ormore mass storage devices, e.g., one or more of a removable disk drive,a hard disk drive, a direct access storage device (DASD), an opticaldrive (e.g., a CD drive, a DVD drive, etc.), a solid state storage drive(SSD), network attached storage, a storage area network, and a tapedrive, among others. Furthermore, vehicle 100 may include a userinterface 164 to enable vehicle 100 to receive a number of inputs fromand generate outputs for a user or operator, e.g., one or more displays,touchscreens, voice interfaces, gesture interfaces, buttons and othertactile controls, etc. Otherwise, user input may be received via anothercomputer or electronic device, e.g., via an app on a mobile device orvia a web interface.

Moreover, vehicle 100 may include one or more network interfaces, e.g.,network interface 162, suitable for communicating with one or morenetworks (e.g., one or more of a LAN, a WAN, a wireless network, and theInternet, among others) to permit the communication of information withother computers and electronic devices, including, for example, acentral service, such as a cloud service, from which vehicle 100receives environmental and other data for use in autonomous controlthereof.

Each processor illustrated in FIG. 1 , as well as various additionalcontrollers and subsystems disclosed herein, generally operates underthe control of an operating system and executes or otherwise relies uponvarious computer software applications, components, programs, objects,modules, data structures, etc., as will be described in greater detailbelow. Moreover, various applications, components, programs, objects,modules, etc. may also execute on one or more processors in anothercomputer coupled to vehicle 100 via network, e.g., in a distributed,cloud-based, or client-server computing environment, whereby theprocessing required to implement the functions of a computer program maybe allocated to multiple computers or services over a network.

In general, the routines executed to implement the variousimplementations described herein, whether implemented as part of anoperating system or a specific application, component, program, object,module or sequence of instructions, or even a subset thereof, will bereferred to herein as “program code.” Program code typically comprisesone or more instructions that are resident at various times in variousmemory and storage devices, and that, when read and executed by one ormore processors, perform the steps necessary to execute steps orelements embodying the various aspects of the invention. Moreover, whileimplementations have and hereinafter will be described in the context offully functioning computers and systems, it will be appreciated that thevarious implementations described herein are capable of beingdistributed as a program product in a variety of forms, and thatimplementations may be implemented regardless of the particular type ofcomputer readable media used to actually carry out the distribution.Examples of computer readable media include tangible, non-transitorymedia such as volatile and non-volatile memory devices, floppy and otherremovable disks, solid state drives, hard disk drives, magnetic tape,and optical disks (e.g., CD-ROMs, DVDs, etc.), among others.

In addition, various program code described hereinafter may beidentified based upon the application within which it is implemented ina specific implementation. However, it should be appreciated that anyparticular program nomenclature that follows is used merely forconvenience, and thus the invention should not be limited to use solelyin any specific application identified or implied by such nomenclature.Furthermore, given the typically endless number of manners in whichcomputer programs may be organized into routines, procedures, methods,modules, objects, and the like, as well as the various manners in whichprogram functionality may be allocated among various software layersthat are resident within a typical computer (e.g., operating systems,libraries, API's, applications, applets, etc.), it should be appreciatedthat the invention is not limited to the specific organization andallocation of program functionality described herein.

MIMO Radar Sensors

FIG. 2 next illustrates an example radar sensor 200 within which thevarious techniques described herein may be implemented. In someimplementations, radar sensor 200 may be a distributed radar sensor. Insome implementations, sensor 200 includes one or more MIMO radartransceivers (e.g., transceivers 202A and 202B) coupled to a controller204, with each MIMO radar transceiver generally including multipletransmit (Tx) antennas (e.g., transmit antennas 206A, 206B) and multiplereceive (Rx) antennas (e.g., receive antennas 208A, 208B) to implement aphased antenna array.

Each transceiver 202A, 202B may be disposed on a separate integratedcircuit (IC) or chip in some implementations, while in otherimplementations multiple transceivers may be disposed on the same chip.Further, multiple transceivers 202A, 202B may be disposed on separate orcommon modules, boards, cards, or housings in various implementations.In addition, it will be appreciated that, rather than utilizingtransceivers that handle both transmission and reception of radarsignals, some implementations may utilize separate circuitry for thesefunctions.

Controller 204 is generally coupled to one or more transceivers. Forexample, controller 204 is coupled to each transceiver 202A, 202B forcontrolling both (i) the generation of radar signals for transmission oremission by transmit antennas 206A, 206B and (ii) the reception andprocessing of radar signals received by receive antennas 208A, 208B. Itwill be appreciated that the functionality implemented by controller 204may be allocated in various manners in different implementations, e.g.,using one or more chips that are separate from each transceiver 202A,202B and disposed on the same or different module, board, card orhousing, or being wholly or partially integrated into the same chips asone or more of the transceivers. The functionality of controller 204 mayalso be at least partially implemented external of any radar sensor insome implementations, e.g., integrated into other processors orcontrollers in the vehicle control system of an autonomous vehicle.Further, while a single controller 204 is illustrated in FIG. 2 , theinvention is not so limited, as multiple controllers may be used toimplement different functionality in a radar sensor in someimplementations, e.g., using multiple controllers integrated with eachtransceiver 202A, 202B. In some implementations, one or more ofcontroller 204 and transceivers 202A, 202B may be implemented using oneor more Monolithic Microwave Integrated Circuits (MMICs).

As such, it will be appreciated that the functionality described hereinmay in some implementations be implemented using various types ofcontrol logic, whether integrated into a transmitter, receiver ortransceiver, processor, controller, computer system, etc., whetherdisposed on one or more integrated circuit chips, and whetherincorporating hardwired logic or programmable logic capable of executingprogram code instructions. Control logic may also be considered toinclude analog circuitry, digital circuitry, or both in variousimplementations. As such, the invention is not limited to the particularcontrol logic implementation details described herein.

Likewise, transmit antennas 206A, 206B and receive antennas 208A, 208Bmay be implemented in a wide variety of manners, e.g., as patch antennasdisposed on one or more printed circuit boards or cards, or in someinstances disposed on or in a package or chip and thus integrated with atransceiver or controller of the radar sensor, e.g., using antenna onpackaging (AOP) or antenna on chip (AOC) technology. Antennas 206A,206B, 208A, 208B may be omnidirectional or directional in differentimplementations. In some implementations, the same antennas may be usedfor both transmit and receive; however, in the illustratedimplementations, separate antennas are used to handle the transmissionand reception of radar signals. Therefore, a reference to an antenna asbeing a transmit antenna or a receive antenna herein does notnecessarily require that the antenna be used exclusively for thatpurpose.

Antennas 206A, 206B, 208A, 208B in the illustrated implementations aredesirably physical arranged and electronically controlled to implement aMIMO virtual antenna array (VAA), i.e., an array of virtual arrayelements that individually represent unique transmit/receive antennapairs. FIG. 3 , for example, illustrates an example virtual antennaarray 220 formed from a set of three physical transmit antennas 222(Tx1, Tx2, Tx3, each of which corresponding, for example, to a transmitantenna 206A, 206B in FIG. 2 ) and four physical receive antennas 224(Rx1, Rx2, Rx3, Rx4, each of which corresponding, for example, to areceive antenna 208A, 208B in FIG. 2 ), which together form a virtualantenna array having a 3×4 or 12 element array of virtual array elements226, thereby increasing the effective number of antennas and improvingcross-range resolution. It will be appreciated that different numbers orarrangements of physical transmit and receive antennas may be used toform different sizes and arrangements of virtual antenna arrays, so theinvention is not limited to the specific array illustrated in FIG. 3 .

Increasing the numbers of physical transmit antennas and physicalreceive antennas for a virtual antenna array, and thus the number ofvirtual array elements in the virtual antenna array, may generally beused to increase angular resolution, detection range, or signal to noiseratio. In one example implementation, an individual transceiver chiphaving three transmit antennas and four receive antennas may be used toform a virtual antenna array having twelve virtual array elements, whichmay, in some instances, be used to form a one dimensional array of <5 cmlength (e.g., emphasizing azimuth resolution) or in other instances forma two dimensional of at most about 1 cm×1 cm (e.g., providing coarseresolution in both azimuth and elevation). If four of such transceiverchips are used in the same virtual antenna array, however, a total of 12transmit antennas and 16 receive antennas may be used to generate 192virtual array elements. Such element counts may be used for example, togenerate two dimensional array layouts over about a 10 cm×10 cm area,and allowing for an angular resolution of a few degrees in both azimuthand elevation.

Now turning to FIGS. 4 and 5 , these figures respectively illustrateexample transmit and receive channels or paths for individual transmitand receive antennas 206A, 206B, 208A, 208B in transceiver 202A (itbeing understood that similar components may be used for othertransceivers such as transceiver 202B). While the techniques describedherein may be applicable to pulse modulated radar sensors or any othertypes of radar sensors, the illustrated implementations will focus onMIMO radar sensors that utilize millimeter wave frequency modulatedcontinuous wave (FMCW) radar signals.

In the transmit channel of transceiver 202A as illustrated in FIG. 4 , alocal oscillator (LO) 230 generates an FMCW radio frequency (RF) signal,e.g., in the range of 76 GHz to 81 GHz. The FMCW RF signal is amplifiedby an amplifier 232 to drive a transmit antenna 206A. The frequency ofLO 230 is determined by a modulator block 234, which is capable offrequency modulating LO 230 to effectively generate pulsed signals orsweep signals referred to as chirps, e.g., using sawtooth or anotherform of frequency modulation. Control over modulator block 234 may beprovided by a controller 236, which in some instances may be controller204, while in other instances may be other control logic, e.g., as maybe integrated into transceiver 202A. Controller 236 may be used tocontrol various parameters of the chirps, e.g., start frequency, phase,chirp rate, etc., as well as to trigger the initiation of a chirp.

In the receive channel of transceiver 202A as illustrated in FIG. 5 , areceived RF signal from an antenna 208A is amplified by an amplifier 238and then mixed with the LO 230 signal by a mixer 240 to generate a mixedsignal. The mixed signal is filtered by a filter 242 and digitized by ananalog to digital converter (ADC) 244 to generate a stream of digitalsignals. For example, the digital signals can be data samples, which inthe illustrated implementation may be considered to be digital valuesoutput by ADC 244, and which may in some implementations include otheridentifying data such as the channel, transmit antenna, receive antenna,chirp number, timestamp, etc. associated with the digital value. Thedigital signals are provided to controller 236.

It will be appreciated that in different implementations, variouscomponents among components 230-244 of FIGS. 4 and 5 may be shared bymultiple transmit channels or multiple receive channels and thatmultiple instances of some components may be dedicated to differentchannels. Further, other architectures may be used to implement transmitchannels or receive channels in other implementations, so the inventionis not limited to the specific architecture illustrated in FIGS. 4-5 .In addition, in some implementations, controller 236 may be replaced bycontroller 204 of radar sensor 200. In these implementations, controller204 of radar sensor 200 may control one or more components of components230-244 described with reference to FIGS. 4 and 5 .

FIG. 6 next illustrates diagrams showing general operations of a radarsensor and data generated by the radar sensor. For example, the radarsensor may be an FMCW MIMO radar sensor such as radar sensor 200discussed above in connection with FIGS. 2-5 . Graph 252, for example,illustrates a simplified time vs. frequency graph of a sequence ofchirps. A chirp may represent a sweeping signal across frequency in acertain cycle. For example, a chirp CH1 is a sweeping signal duringcycle C1, a chirp CH2 is a sweeping signal during cycle C2, and a chirpCH3 is a sweeping signal during cycle C3. In this example, chirpsCH1-CH3 are illustrated as repetitions of sweeping signals having thesame shape. However, in some implementations, chirps may dwindle overtime. In addition, in this example graph, chirps C1-C3 are linearlymodulated to have a sawtooth shape. However, in some implementations,the chirps may be modulated non-linearly or may be modulated to have anyshape. Graph 252 shows both a transmitted signal 254 (which matches thefrequency of the local oscillator) for a transmit channel Tx andreceived signals 256, 258 for two targets located at difference rangesand received by a receive channel Rx. In this example, the transmittedsignal 254 represents a sequence of chirps. As shown in this graph, thetime delay from transmission of the transmit signal to being receivedfor the two targets causes a difference in frequency, e.g., illustratedby D1 for a first target and D2 for a second target.

In some implementations, data samples collected by radar sensor 200 maybe processed to generate radar data associated with certain features.For example, the radar data may be represented as data cubes associatedwith certain features. The features may be represented as dimensions ofthe data cubes where the features include, but are not limited to, fasttime (the number of samples in one chirp), slow time (the number ofchirps in one set of chirps), and the number of receive channels. Wherea local oscillator is operated at about 77 GHz, a controller (e.g.,controller 204 in FIG. 2 or controller 236 in FIGS. 4 and 5 ) mayprocess received data samples such that each frame may include 128-512chirps and 512-1024 samples per chirp. In this example, a frame firingduration (also referred to as a coherent processing interval (CPI) maybe about 5-15 ms/frame, a sample rate may be about 20 millionsamples/second, and a chirp duration may be about 25-100 microsecondsper chirp. In some implementations, receive channels (e.g., about 4-16Rx channels) may be processed in parallel. Although specific numbers areprovided in this paragraph, they are provided as examples and anysuitable numbers can be used to implement radar sensors.

Radar data (e.g., data cubes) may be processed to determine, for one ormore targets in the field of view of a radar sensor, (i) range from theradar sensor to a respective target, (ii) Doppler velocity (i.e., radialvelocity of the respective target relative to the radar sensor), or(iii) angle of arrival, in terms of one or both of azimuth andelevation. First, as illustrated at 260, sampling may be performed oneach receive channel over multiple chirps in a frame or CPI. The samplesfor all of the chirps in the frame for a particular Tx/Rx pair may beincorporated into a two dimensional array 262 where the samples arearranged in one dimension by sample number (vertical axis of FIG. 6 ,from first sample to last sample collected for each chirp) and inanother dimension by chirp number (horizontal axis of FIG. 6 , fromfirst chirp to last chirp in a frame). In one example implementation,for example, where a frame includes 128 chirps with 1024 samples in eachchirp, the array may have dimensions of 128 (horizontal)×1024(vertical).

Next, range measurements are determined for the samples in each channel,generally by performing a Fast Fourier Transform (FFT) operation 264(referred to herein as a range FFT), or other frequency transformation,which recovers the frequency spectrum from the digital samples in eachchannel to generate a range profile (power vs. range) in the frequencydomain for each chirp for a particular Tx/Rx pair. It will beappreciated, in particular, that a target at a given range from a radarsensor will delay the transmitted signal 254 by a delay that isproportional to its range, and that this delay remains substantiallyconstant over a chirp. Given that the mixed signal output by mixer 240of FIG. 5 is effectively the difference in the instantaneous frequenciesof the transmitted and received signals within a given channel, and thatthis difference is substantially constant over a chirp, the reflectioncorresponding to the target effectively generates a constant frequency“tone” in the mixed signal that resolves to a peak in the frequencydomain at that frequency. Multiple targets therefore resolve to a rangeprofile having different peaks in the frequency domain corresponding tothe ranges of those targets, and may be grouped in some implementationsinto frequency bins corresponding to different ranges in the field ofview of the radar sensor.

Each range profile for a particular chirp may be considered to be a onedimensional array representing power over a range of frequencies forthat chirp. The range profiles for the chirps in the frame may thereforealso be stacked into an array 266 having one dimension representingranging frequency or frequency bin (vertical axis in FIG. 6 ) and onedimension representing chirp number (horizontal axis in FIG. 6 ), and itmay be seen by the representation of array 266 that horizontal linesgenerally represent frequency bins where potential targets at variousranges corresponding to those frequency bins have been detected over thecourse of multiple chirps in a frame.

Next, velocity measurements (e.g., Doppler measurements) are determinedfor the samples in each channel, generally by performing a second FFToperation 268 (referred to herein as a Doppler FFT) to recover phaseinformation corresponding to Doppler shifts. Transforming across the setof chirps results in a data set that may be represented by an array 270arranged by ranging frequency or frequency bin (vertical axis) andDoppler frequency or frequency bin (horizontal axis), where each Dopplerfrequency bin generally corresponds to a particular velocity for apotential target disposed within a particular range frequency bin.

Next, beamforming is performed to determine angle of arrivalinformation. It should be noted that arrays 262, 266 and 270 are eachbased on the samples for a single transmit channel/receive channel(Tx/Rx) pair. Thus, a stacking operation 272 may be performed to stackthe arrays 270 generated by the Doppler FFT operation for differentTx/Rx pairs (also referred to as array elements) into a data stack 274.

It will be appreciated that each different Tx/Rx pair may have adifferent spatial relationship between the respective physical transmitand receive antennas for the pair, which can lead to slightly differentphases reported for the same target for different Tx/Rx pairs. In thecase of a uniform linear array, a third FFT operation 276 (referred toherein as a beamforming FFT) may therefore use the set of values acrossthe different array elements in stack 274 (also referred as abeamvector) to estimate an angle of arrival at each range-Dopplerlocation (i.e., each combination of range frequency bin and Dopplerfrequency bin). More generally, a set of complex responses expected forsome set of azimuth and elevation angles of arrival, also known assteering vectors, may be multiplied onto the beamvectors to generateazimuth and elevation angles for each target (represented by graphs278).

Then, the aforementioned range, Doppler and angle of arrival informationmay be combined in some implementations by a point cloud generationoperation 280 into a three dimensional point cloud 282 includingestimated position (e.g., using cartesian or polar coordinates),velocity, and signal intensity (or confidence) for a plurality oftargets in the field of view of the radar sensor.

It will be appreciated that a wide variety of modifications andenhancements may be made to the aforementioned operations of FIG. 6 , sothe invention is not limited to this specific sequence of operations.

Those skilled in the art, having the benefit of the present disclosure,will recognize that the exemplary environment illustrated in FIGS. 1-6is not intended to limit implementations disclosed herein. Indeed, thoseskilled in the art will recognize that other alternative hardware orsoftware environments may be used without departing from the scope ofimplementations disclosed herein. It will also be appreciated that thevarious MIMO radar techniques described herein may be utilized inconnection with other applications, so the invention is not limited toMIMO radars or radar sensing systems used solely in connection with thecontrol of an autonomous vehicle.

Distributed Radar Antenna Array Aperture

Now turning to FIG. 7 , it may be desirable in some implementations toutilize multiple MIMO radar sensors or units having overlapping fieldsof view to effectively synthesize a distributed radar antenna arrayaperture from the outputs of the multiple radar sensors and effectivelyenhance one or more of angular resolution, range, and signal to noiseratio beyond that supported by any of the radar sensors individually. Insome instances, and as will be discussed in greater detail below, thetechniques described herein may be used to enable radar sensors thatotherwise would have insufficient angular resolution on their own toadequately discriminate between various objects in the environment of anautonomous or other vehicle to be used collectively by a vehicle controlsystem in connection with the autonomous control of a vehicle.

Specifically, FIG. 7 illustrates a multi-sensor radar system 300including a plurality of MIMO radar sensors or units (e.g., radarsensors 302A, 302B) operably coupled to one another by a controller 304.Each radar sensor 302A, 302B may be implemented as a MIMO radar sensorhaving one or more transmit (Tx) antennas (e.g., transmit antennas 306A,306B) and one or more receive (Rx) antennas (e.g., receive antennas308A, 308B) that form respective virtual antenna arrays 310A, 310B,which in the illustrated implementation are two dimensional virtualantenna arrays, although the invention is not so limited in otherimplementations. In some implementations, each radar sensor 302A, 302Bmay, for example, be implemented in any of the various manners discussedabove for radar sensor 200 of FIGS. 2-6 , and as will become moreapparent below, the virtual antenna arrays 310A, 310B may be used toeffectively synthesize a distributed radar antenna array aperture 312that is effectively larger than and has an improved angular resolutionover one or more of the individual virtual antenna arrays 310A, 310B.Controller 304, in the illustrated implementation, is separate from eachMIMO radar sensor or unit 302A, 302B, and in some instances, may bededicated controller, or in other instances, may be integrated intoanother controller or control system, e.g., the vehicle control systemof an autonomous vehicle in an autonomous vehicle application. In otherimplementations, controller 304 may be implemented in other controllersor control systems, whether local to or remote from the radar sensors,while in other implementations, the controller may be integrated whollyor partially into one or more of the radar sensors 302A, 302B. Othervariations will be apparatus to those of ordinary skill having thebenefit of the instant disclosure.

It will be appreciated that coherently fusing the apertures frommultiple distributed virtual antenna sub-arrays, e.g., in cascaded radarsystems, generally requires the use of a common Local Oscillator (LO)signal shared across all the transmitters and receivers in the entirecascade system. A master module in such a system generally controls theradar chirp/frame timing for all of the chips and modules in the systemby generating a digital synchronization signal and sharing thissynchronization signal with other, slave radar modules. The mastermodule also generally is required to generate an oscillator clock andshare it with the slave modules to ensure that the entire systemoperates from a single clock source. It has been found, however, thatsuch synchronization is expensive and difficult to achieve, particularlywhen trying to generate a large (e.g., greater than 30 cm) synchronizedaperture. Given that in many automotive applications, LO signals of 20+GHz are used, sharing such high frequency signals across different chipsor hardware modules is ordinarily unachievable without the use ofspecialized and expensive circuit boards and materials.

In the herein-described implementations, however, a coherent distributedarray radar (DAR) may be generated without the use of any LO or clocksynchronization among the radar sub-arrays, by coherently comparing theresidual phases after range-doppler extraction from all the channels inthe whole DAR system. It has been found, in particular, that theresidual phases in the Tx/Rx channels are linearly proportional to theranges between the antennas and the targets. Thus, within physical andtemporal alignment constraints that are generally achievable in typicalautomotive environments, a phase gradient approach may be used to derivean element-wise phase correction function to address such misalignments.Such an approach is data driven and capable of adequately correctingshifts/rotations of different physical apertures relative to oneanother, thereby allowing multiple radar sub-arrays to synthesize adistributed array radar (DAR) having enhanced angular resolution,enhanced detection range, and enhanced signal to noise ratio.

In some implementations, as noted above, the different radar sub-arraysmay be implemented using separate radar sensors or units havingoverlapping fields of view, e.g., separate radar sensors mounted inrelative proximity to one another and facing in the same generaldirection, such that at least a portion of the field of view of eachradar sensor overlaps that of the other radar sensor. While it isgenerally desirable to position the radar sensors in fixed locationsrelative to one another such that the spatial relationship between thedifferent radar sensors both fixed and known, the precision of thespatial relationships is generally not required to be great, such that,for example, multiple radar sensors may be mounted on a common bracketor on predetermined locations on an automobile to achieve sufficientspatial positioning to synthesize an aperture with enhanced angularresolution, enhanced detection range, and enhanced signal to noiseratio. Moreover, the temporal relationship between different radarsensors is also desirably controlled, e.g., by using a trigger signal orother synchronization signal (e.g., a Precision Time Protocol (PTP)signal) that synchronizes one or both of the sensing frames and chirpsemitted by the radar sensors. The precision required to provide adequatetemporal alignment between the different radar sensors, however, is wellbelow that which would be achieved with a shared LO or clock signal.

Moreover, as noted above, in some implementations, the radar sensorslack a common LO signal or clock signal, and in some implementations,the radar sensors may be completely separate units that operateindependently and generate independent outputs that may be synthesizedto form the distributed aperture, e.g., using a controller that isseparate from any of the radar sensors. Thus, the radar sensors may beconsidered to operate using separate local oscillators and separateclocks. Further, in some implementations, radar sensors predominantlyused for lower resolution automotive applications such as foot-activatedtailgates, adaptive cruise control, lane change assist, and other driverassistance applications may be utilized in a DAR system using theherein-described techniques. It will be appreciated that such radarsensors, when used alone, generally lack sufficient angular resolution,detection range, or signal to noise ratio for higher resolution radarimaging applications.

In some implementations, data is captured independently on separateradar sensors, a set of expected array responses for various angles(e.g., steering vectors) is generated across the distributed apertureaccording to all of the channels' relative locations using calculatedresidual phase relations, and angles may be extracted using the steeringvectors to obtain the enhanced resolution from the distributed aperture.

FIG. 8 , for example, illustrates an example sequence of operations 320for sensing objects in an environment using multi-sensor radar system300 of FIG. 7 , e.g., as may be implemented in controller 304. As shownin blocks 322, each radar sensor may provide, e.g., for each sensingframe, point data and beamforming data (e.g., associated beamvectors)generated by the radar sensor during that sensing frame. The point data,for example, may include positional information for one or more points(also referred to herein as targets). In some instances, the point datamay be provided as point cloud data that identifies one or more pointsor targets, and for each point or target, a position (e.g., in cartesianor polar coordinates), a velocity and an intensity. Other radar sensoroutput data that may be used to identify at least the position of apoint or target sensed by the radar sensor may be provided in thealternative, e.g., based upon the particular radar sensor used, as willbe appreciated by those of ordinary skill having the benefit of theinstant disclosure, and it will be appreciated that other manners ofrepresenting a point's position, e.g., a localized multi-dimensionalspectrum in the neighborhood of a detection, may be used in otherimplementations. It will also be appreciated that while in theillustrated implementation both velocity and intensity are provided foreach sensed point, in other implementations, no velocity or intensityinformation may be provided.

In addition, in the illustrated implementation, each radar sensor alsoprovides beamforming data (e.g., beamvector data) associated with one ormore angles of arrival (e.g., elevation, azimuth or both) for one ormore points or targets sensed by the radar sensor. In someimplementations, for example, the beamforming information may include abeamvector for each target, such as the set of values used for abeamforming operation across the different array elements associatedwith various receive channels for the radar sensor for the range-Dopplerlocation associated with the target. Other manners of representingbeamforming information associated with one or more points reported by aradar sensor may be used in other implementations. In someimplementations, given the similarity in chirp/data acquisitionparameters as the radar sensors are operated concurrently, therange-Doppler spaces for all of the radar sensors may be aligned toeffectively associate the beamvector data from each radar sensor, whichwhile being more data intensive may provide a more complete solutionthat considers all of the beamvectors from the range-Doppler spacerather than just those beamvectors associated with detected andassociated points. By providing only the beamforming data associatedwith detected points, however, the amount of data that each radar sensoris required to report is substantially reduced.

It will be appreciated that beamforming data in some implementations maybe considered to be a form of intermediate data that is generally notreported by some radar sensors. As such, it may be desirable in someimplementations to modify the radar sensors to report such intermediatedata. Also, where each radar sensor has a variable sensing threshold, itmay be desirable to reduce the sensing threshold on each sensor toinclude marginal points (e.g., points with one or both of lowerintensity and lower confidence levels) that may benefit from theincreased signal-to-noise ratio achieved with the herein-describedtechniques.

Based upon the aforementioned information collected from the radarsensors, block 324 identifies a set of one or more correlated points,i.e., points identified by at least two of the different radar sensorsthat with at least some confidence are considered to be associated withthe same real world object. The correlated points may be identified insome implementations using a nearest neighbor spatial matchingalgorithm, or another suitable algorithm. In some implementations, thespatial matching may be based on range, Doppler and anglecorrespondence, although in other implementations other factors, e.g.,intensity, may also be considered, while in other implementations,spatial matching may only be based on a subset of such factors, e.g.,based upon matching one or both of range and Doppler bins to associatebeamvectors. Further, in some implementations, points may be weightedbased upon one or both of intensity and field of view, e.g., toprioritize points that are more intense, have higher confidences or arecloser to the center of the field of view, or to ignore points that areless intense, are of lower confidence or are positioned more towards theperiphery of the field of view (which may focus performance enhancementson the portions of the field of view that are of potentially greaterrelevance).

Next, in block 326, one or more points from the set of correlated pointsare used along with an ideal array geometry for one of the radar sensorsto create a set of ideal beamvectors for the concatenated array. Inparticular, based upon a known relative position of each antenna in eachradar sensor (based, in part, on a known position of each antenna ineach radar sensor as well as the known position of each radar sensorrelative to the other radar sensors), a set of ideal beamvectors for theconcatenated array may be determined, e.g., by calculating the relativephase shifts across all the antenna elements in the MIMO virtual array,which is generally proportional to the transmission delay between theassociated points to the antenna elements. For example, in someimplementations, the relative phases for a point at a certain azimuth,elevation and distant range may be calculated for each Tx/Rx pair bycalculating the distance from the Tx antenna to the point back to the Rxantenna, e.g., using the equation phase=2π*(total_range)/wavelength. Theprocess may be repeated for all Tx/Rx pairs. The point position for theparticular azimuth/elevation is also constant.

Next, in block 328, a phase correction may be derived that compensatesfor one or both of temporal and spatial sub-array mismatches through acomparison between the set of ideal beamvectors and the actualbeamvectors for each sub-array, e.g., by first correlating the idealbeamvectors and actual beamvectors to estimate the phase correction, andthen removing any linear phase shift components in the estimated phasecorrections, as it will be appreciated that after range and Dopplerextraction, the dominating residual phase of a receive channel signalwill be linearly proportional to the distance from the antenna to thetarget.

In particular, in some implementations, and assuming for example a radarsystem incorporating two radar sensors, for each return associatedbetween the two radar sensors, A may be considered to be the idealizedbeamvector and B may be considered to be the concatenated beamvectors ofthe two radar sensors. A phase difference may be calculated is theequation A*complex_conjugate(B)), and as such, the phase difference maybe a unitary complex vector the same length as A and B. This process maybe performed to compute the estimated disparity between each ideal andmeasured set. It will be appreciated, however, that each disparity comeswith an additional random phase offset, so simply averaging the phasedifferences may not provide a suitable estimate. As such, in someimplementations, a phase gradient (i.e., a derivative) of each may becomputed, which may remove the additional random phase and allow the setto be averaged. After averaging, the estimate may be re-integrated toform the final phase correction.

Next, in block 330, the determined phase correction is applied, and thenthe beamforming process (e.g., a beamforming FFT operation) is repeatedfor the reported points from one or more of the radar sensors. Then, asillustrated in block 332, one or more reported points may be refined(i.e., such that the concatenated array improves the positional accuracyof the positional information for a point), one or more additionalpoints may be determined (i.e., such that the concatenated arrayidentifies one or more points that were not identified by one or more ofthe sub-arrays), or both. An example of the former improvement is one inwhich the angle of arrival for a point is refined to a more accurateposition, while an example of the latter improvement is one in whichpoints that were determined to be the same target from multiplesub-arrays are instead determined to be for different targets havingdifferent corresponding angles of arrival. As such, a point cloud 334(or other suitable radar output format) including the refined oradditional points may be reported by block 332.

Thus, a combined point cloud (or other suitable radar output) may bereported by system 300, representing the target(s) collectively sensedby the multiple individual radar sensors 302A, 302B, and generally withenhanced angular resolution, enhanced detection range and enhancedsignal-to-noise ratio.

Other variations will be apparent to those of ordinary skill. Therefore,the invention lies in the claims hereinafter appended.

What is claimed is:
 1. A method, comprising: receiving first radar datafrom a first multiple input multiple output (MIMO) radar sensor disposedon a vehicle, the first MIMO radar sensor including one or more transmitantennas and one or more receive antennas forming a first radarsub-array, and the first radar data including first point dataidentifying one or more points sensed by the first MIMO radar sensor andfirst beamforming data from the first radar sub-array; receiving secondradar data from a second MIMO radar sensor disposed on the vehicle, thesecond MIMO radar sensor having a field of view that overlaps with thatof the first MIMO radar sensor and including one or more transmitantennas and one or more receive antennas forming a second radarsub-array, and the second radar data including second point dataidentifying one or more points sensed by the second MIMO radar sensorand second beamforming data from the second radar sub-array; andsynthesizing a distributed array from the first and second radarsub-arrays by applying a phase correction that compensates for temporalor spatial mismatches between the first and second radar sub-arraysusing the first and second point data and the first and secondbeamforming data and thereafter performing a beamforming operation onone or more points in the first or second point data after the phasecorrection is applied to refine the one or more points; wherein thefirst beamforming data includes an actual beamvector for each of the oneor more points sensed by the first MIMO radar sensor and the secondbeamforming data includes an actual beamvector for each of the one ormore points sensed by the second MIMO radar sensor, wherein synthesizingthe distributed array further includes identifying one or morecorrelated points from the first and second point data, and whereinapplying the phase correction includes: generating a set of idealbeamvectors for one of the first and second radar sensors based upon theidentified one or more correlated points; and generating the phasecorrection by comparing the set of ideal beamvectors to the actualbeamvectors in the first and second beamforming data.
 2. The method ofclaim 1, wherein the first and second MIMO radar sensors operate usingseparate local oscillators.
 3. The method of claim 1, wherein the firstand second MIMO radar sensors operate using separate clocks.
 4. Themethod of claim 1, wherein the first point data includes a point cloud,the point cloud identifying a position and a velocity for each of theone or more points sensed by the first MIMO radar sensor.
 5. The methodof claim 1, wherein generating the set of ideal beamvectors includes,for a first point from the one or more correlated points, calculating arelative phase for each of a plurality of transmitter/receiver pairs,wherein calculating the relative phase for each of the plurality oftransmitter/receiver pairs includes, for a first transmitter/receiverpair of the plurality of transmitter receiver pairs, calculating adistance from a first transmitter of the first transmitter/receiver pairto the first point and to a first receiver of the firsttransmitter/receiver pair, and wherein comparing the set of idealbeamvectors to the actual beamvectors in the first and secondbeamforming data includes: correlating the set of ideal beamvectors withthe actual beamvectors in the first and second beamforming data toestimate a phase correction; and removing a linear phase shift componentfrom the estimated phase correction.
 6. The method of claim 5, whereincorrelating the set of ideal beamvectors with the actual beamvectors inthe first and second beamforming data to estimate the phase correctionincludes determining an estimated disparity between the set of idealbeamvectors and the actual beamvectors in the first and secondbeamforming data and calculating a phase gradient of the estimateddisparity to remove a random phase offset.
 7. The method of claim 6,wherein performing the beamforming operation refines a position of atleast one of the one or more points in the first or second point data.8. The method of claim 6, wherein performing the beamforming operationdetermines an additional point.
 9. The method of claim 6, whereinidentifying the one or more correlated points is performed using anearest neighbor spatial matching algorithm based on range, Doppler andangle of arrival correspondence between points in the first and secondpoint data.
 10. A vehicle radar system, comprising: a memory; one ormore processors; and program code resident in the memory and configuredupon execution by the one or more processors to: receive first radardata from a first multiple input multiple output (MIMO) radar sensordisposed on the vehicle, the first MIMO radar sensor including one ormore transmit antennas and one or more receive antennas forming a firstradar sub-array, and the first radar data including first point dataidentifying one or more points sensed by the first MIMO radar sensor andfirst beamforming data from the first radar sub-array, wherein the firstbeamforming data is generated by performing a first beamformingoperation in the first MIMO radar sensor; receive second radar data froma second MIMO radar sensor disposed on the vehicle, the second MIMOradar sensor having a field of view that overlaps with that of the firstMIMO radar sensor and including one or more transmit antennas and one ormore receive antennas forming a second radar sub-array, and the secondradar data including second point data identifying one or more pointssensed by the second MIMO radar sensor and second beamforming data fromthe second radar sub-array, wherein the second beamforming data isgenerated by performing a second beamforming operation in the secondMIMO radar sensor; and synthesize a distributed array from the first andsecond radar sub-arrays by applying a phase correction that compensatesfor temporal or spatial mismatches between the first and second radarsub-arrays using the first and second point data and the first andsecond beamforming data and thereafter performing a repeated beamformingoperation on one or more points in the first or second point data afterthe phase correction is applied to refine the one or more points. 11.The vehicle radar system of claim 10, further comprising the first MIMOradar sensor.
 12. The vehicle radar system of claim 11, wherein the oneor more processors are disposed in the first MIMO radar sensor.
 13. Thevehicle radar system of claim 11, further comprising the second MIMOradar sensor, wherein the one or more processors are disposed externalof each of the first and second MIMO radar sensors.
 14. The vehicleradar system of claim 10, wherein the first point data includes a pointcloud, the point cloud identifying a position and a velocity for each ofthe one or more points sensed by the first MIMO radar sensor.
 15. Thevehicle radar system of claim 10, wherein the first beamforming dataincludes an actual beamvector for each of the one or more points sensedby the first MIMO radar sensor and the second beamforming data includesan actual beamvector for each of the one or more points sensed by thesecond MIMO radar sensor.
 16. The vehicle radar system of claim 15,wherein the program code is configured to synthesize the distributedarray further by identifying one or more correlated points from thefirst and second point data, and wherein the program code is configuredto apply the phase correction by: generating a set of ideal beamvectorsfor one of the first and second radar sensors based upon the identifiedone or more correlated points; and generating the phase correction bycomparing the set of ideal beamvectors to the actual beamvectors in thefirst and second beamforming data.
 17. The vehicle radar system of claim16, wherein the repeated beamforming operation refines a position of atleast one of the one or more points in the first or second point data.18. The vehicle radar system of claim 16, wherein the repeatedbeamforming operation determines an additional point.
 19. The vehicleradar system of claim 16, wherein the program code is configured toidentify the one or more correlated points using a nearest neighborspatial matching algorithm based on range, Doppler and angle of arrivalcorrespondence between points in the first and second point data.
 20. Aprogram product, comprising: a non-transitory computer readable medium;and program code stored on the non-transitory computer readable mediumand configured upon execution by one or more processors to: receivefirst radar data from a first multiple input multiple output (MIMO)radar sensor disposed on the vehicle, the first MIMO radar sensorincluding one or more transmit antennas and one or more receive antennasforming a first radar sub-array, and the first radar data includingfirst point data identifying one or more points sensed by the first MIMOradar sensor and first beamforming data from the first radar sub-array,wherein the first beamforming data is generated by performing a firstbeamforming operation in the first MIMO radar sensor; receive secondradar data from a second MIMO radar sensor disposed on the vehicle, thesecond MIMO radar sensor having a field of view that overlaps with thatof the first MIMO radar sensor and including one or more transmitantennas and one or more receive antennas forming a second radarsub-array, and the second radar data including second point dataidentifying one or more points sensed by the second MIMO radar sensorand second beamforming data from the second radar sub-array, wherein thefirst beamforming data is generated by performing a first beamformingoperation in the first MIMO radar sensor; and synthesize a distributedarray from the first and second radar sub-arrays by applying a phasecorrection that compensates for temporal or spatial mismatches betweenthe first and second radar sub-arrays using the first and second pointdata and the first and second beamforming data and thereafter performinga repeated beamforming operation on one or more points in the first orsecond point data after the phase correction is applied to refine theone or more points.